126 lines
5.0 KiB
Python
126 lines
5.0 KiB
Python
import math
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import unittest
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import numpy as np
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import torch
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from tinygrad.tensor import Tensor
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import tinygrad.nn as nn
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import pytest
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from tinygrad.helpers import dtypes
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pytestmark = pytest.mark.webgpu
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# https://gist.github.com/devries/11405101
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def ksprob(a):
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fac, total, termbf = 2.0, 0.0, 0.0
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a2 = -2.0 * a * a
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for j in range(1, 101):
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term = fac * math.exp(a2 * j * j)
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total += term
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if math.fabs(term) <= 0.001 * termbf or math.fabs(term) <= 1e-8 * total:
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return total
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fac = -fac
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termbf = math.fabs(term)
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return 1.0
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def kstest(l1, l2):
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n1, n2 = len(l1), len(l2)
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l1.sort()
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l2.sort()
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j1, j2, d, fn1, fn2 = 0, 0, 0.0, 0.0, 0.0
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while j1 < n1 and j2 < n2:
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d1, d2 = l1[j1], l2[j2]
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if d1 <= d2:
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fn1 = (float(j1) + 1.0) / float(n1)
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j1 += 1
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if d2 <= d1:
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fn2 = (float(j2) + 1.0) / float(n2)
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j2 += 1
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dtemp = math.fabs(fn2 - fn1)
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if dtemp > d:
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d = dtemp
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ne = float(n1 * n2) / float(n1 + n2)
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nesq = math.sqrt(ne)
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prob = ksprob((nesq + 0.12 + 0.11 / nesq) * d)
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return prob
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def normal_test(func, shape=(20, 23), alpha=0.05):
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Tensor.manual_seed(1337)
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np.random.seed(1337)
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x = func(*shape).numpy().flatten()
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y = np.random.randn(*shape).flatten()
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return kstest(x, y) >= alpha
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def equal_distrib_ints(tiny_func, numpy_func, shape=(20, 23), low=-100, high=100, dtype=dtypes.int32, alpha=0.05):
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Tensor.manual_seed(1337)
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np.random.seed(1337)
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x = tiny_func(*shape, low=low, high=high, dtype=dtype).cpu().numpy().flatten()
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y = numpy_func(shape).flatten()
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return kstest(x, y) >= alpha
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def equal_distribution(tiny_func, torch_func, numpy_func=None, shape=(20, 23), alpha=0.05):
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Tensor.manual_seed(1337)
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torch.manual_seed(1337)
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np.random.seed(1337)
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x = tiny_func(*shape).numpy().flatten()
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if numpy_func is not None: y = numpy_func(shape).flatten()
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z = torch_func(shape).numpy().flatten()
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return (numpy_func is None or kstest(x, y) >= alpha) and kstest(x, z) >= alpha
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class TestRandomness(unittest.TestCase):
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def test_rand(self):
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self.assertFalse(normal_test(Tensor.rand))
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self.assertTrue(equal_distribution(Tensor.rand, torch.rand, lambda x: np.random.rand(*x)))
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def test_randn(self):
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self.assertTrue(normal_test(Tensor.randn))
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self.assertTrue(equal_distribution(Tensor.randn, torch.randn, lambda x: np.random.randn(*x)))
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def test_normal(self):
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self.assertTrue(normal_test(Tensor.normal))
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self.assertTrue(equal_distribution(Tensor.normal, lambda x: torch.nn.init.normal_(torch.empty(x), mean=0, std=1), lambda x: np.random.normal(loc=0, scale=1, size=x)))
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def test_uniform(self):
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self.assertFalse(normal_test(Tensor.uniform))
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self.assertTrue(equal_distribution(Tensor.uniform, lambda x: torch.nn.init.uniform_(torch.empty(x), a=-1, b=1), lambda x: np.random.uniform(low=-1, high=1, size=x)))
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self.assertTrue(equal_distrib_ints(Tensor.uniform, lambda x: np.random.randint(low=-100, high=100, size=x)))
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def test_scaled_uniform(self):
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self.assertFalse(normal_test(Tensor.scaled_uniform))
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self.assertTrue(equal_distribution(Tensor.scaled_uniform, lambda x: torch.nn.init.uniform_(torch.empty(x), a=-1, b=1) / math.sqrt(math.prod(x)), lambda x: (np.random.rand(*x) * 2 - 1) / math.sqrt(math.prod(x))))
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def test_glorot_uniform(self):
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self.assertFalse(normal_test(Tensor.glorot_uniform))
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self.assertTrue(equal_distribution(Tensor.glorot_uniform, lambda x: torch.nn.init.xavier_uniform_(torch.empty(x)), lambda x: (np.random.rand(*x) * 2 - 1) * math.sqrt(6 / (x[0] + math.prod(x[1:])))))
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def test_kaiming_uniform(self):
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Tensor.manual_seed(1337)
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torch.manual_seed(1337)
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np.random.seed(1337)
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for shape in [(128, 64, 3, 3), (20, 24)]:
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self.assertTrue(equal_distribution(Tensor.kaiming_uniform, lambda x: torch.nn.init.kaiming_uniform_(torch.empty(x)), shape=shape))
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def test_kaiming_normal(self):
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Tensor.manual_seed(1337)
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torch.manual_seed(1337)
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np.random.seed(1337)
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for shape in [(128, 64, 3, 3), (20, 24)]:
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self.assertTrue(equal_distribution(Tensor.kaiming_normal, lambda x: torch.nn.init.kaiming_normal_(torch.empty(x)), shape=shape))
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def test_conv2d_init(self):
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params = (128, 256, (3,3))
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assert equal_distribution(lambda *_: nn.Conv2d(*params).weight, lambda _: torch.nn.Conv2d(*params).weight.detach())
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assert equal_distribution(lambda *_: nn.Conv2d(*params).bias, lambda _: torch.nn.Conv2d(*params).bias.detach())
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def test_linear_init(self):
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params = (64, 64)
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assert equal_distribution(lambda *_: nn.Linear(*params).weight, lambda _: torch.nn.Linear(*params).weight.detach())
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assert equal_distribution(lambda *_: nn.Linear(*params).bias, lambda _: torch.nn.Linear(*params).bias.detach())
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def test_bn_init(self):
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params = (64,)
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assert equal_distribution(lambda *_: nn.BatchNorm2d(*params).weight, lambda _: torch.nn.BatchNorm2d(*params).weight.detach())
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assert equal_distribution(lambda *_: nn.BatchNorm2d(*params).bias, lambda _: torch.nn.BatchNorm2d(*params).bias.detach())
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if __name__ == "__main__":
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unittest.main()
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